Порівняння методів

	Peace Duration Analysis	Conflict Recurrence Analysis	Militarized Interstate Dispute Analysis	Аналіз виживаності
Галузь	International Relations	International Relations	International Relations	Статистика досліджень
Родина	Survival analysis	Survival analysis	Process / pipeline	Process / pipeline
Рік появи	2003	2004	1996	1958
Автор методу	Conflict-duration literature (e.g., Caroline Hartzell & Matthew Hoddie on post-civil-war peace)	Civil-war recurrence literature (e.g., Barbara F. Walter)	Daniel Jones, Stuart Bremer & J. David Singer (Correlates of War project)	Edward L. Kaplan and Paul Meier
Тип	Time-to-event (survival) analysis of peace spells	Survival/repeated-events analysis of renewed conflict	Coding and statistical analysis of interstate militarized confrontations	Method
Основоположне джерело	Hartzell, C., & Hoddie, M. (2003). Institutionalizing peace: Power sharing and post-civil war conflict management. American Journal of Political Science, 47(2), 318–332.	Walter, B. F. (2004). Does conflict beget conflict? Explaining recurring civil war. Journal of Peace Research, 41(3), 371–388.	Jones, D. M., Bremer, S. A., & Singer, J. D. (1996). Militarized interstate disputes, 1816–1992: Rationale, coding rules, and empirical patterns. Conflict Management and Peace Science, 15(2), 163–213.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Інші назви	Duration of Peace Analysis, Post-Conflict Peace Survival Analysis, Peace Spell Analysis, Time-to-Conflict-Recurrence Analysis	Recurring Civil War Analysis, Conflict Relapse Analysis, Repeated-Conflict Survival Analysis, Conflict Recidivism Analysis	MID Analysis, Militarized Dispute Coding, Correlates of War Dispute Analysis, Dyadic Conflict Onset Analysis	Kaplan-Meier analysis, Cox regression, TTE analysis
Пов'язані	3	3	3	3
Підсумок	Peace duration analysis applies survival (time-to-event) methods to study how long peace lasts after a conflict ends and what makes it endure or collapse. The unit is the post-conflict peace spell, observed from a settlement or cessation until conflict recurs or the observation is censored. Modeling the hazard that peace fails as a function of how the conflict ended and the structural conditions — as in Hartzell and Hoddie's (2003) study of power-sharing after civil war — reveals which arrangements, such as institutionalized power sharing or peacekeeping, lengthen the survival of peace.	Conflict recurrence analysis studies why and when conflicts that have ended return, treating renewed war as a time-to-event outcome. Most civil wars in recent decades have occurred in countries with a prior war, making recurrence a central puzzle. Using survival and repeated-events models — as in Barbara Walter's (2004) analysis of recurring civil war — researchers model the hazard that a post-conflict country relapses into violence as a function of how the war ended and the underlying conditions, while accounting for the fact that the same country can experience multiple conflict spells.	Militarized interstate dispute (MID) analysis is the coding and quantitative study of confrontations in which one state threatens, displays, or uses military force against another. Built on the Correlates of War project's MID dataset and the coding rules codified by Jones, Bremer, and Singer (1996), it provides the standard observational measure of interstate conflict short of and including war, structured as dyad-years so that the onset, escalation, and outcomes of disputes can be modeled statistically across two centuries of the international system.	Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.